Abstract

Background

Lidar height data collected by the Geosciences Laser Altimeter System (GLAS) from
2002 to 2008 has the potential to form the basis of a globally consistent sample-based
inventory of forest biomass. GLAS lidar return data were collected globally in spatially
discrete full waveform “shots,” which have been shown to be strongly correlated with
aboveground forest biomass. Relationships observed at spatially coincident field plots
may be used to model biomass at all GLAS shots, and well-established methods of model-based
inference may then be used to estimate biomass and variance for specific spatial domains.
However, the spatial pattern of GLAS acquisition is neither random across the surface
of the earth nor is it identifiable with any particular systematic design. Undefined
sample properties therefore hinder the use of GLAS in global forest sampling.

Results

We propose a method of identifying a subset of the GLAS data which can justifiably
be treated as a simple random sample in model-based biomass estimation. The relatively
uniform spatial distribution and locally arbitrary positioning of the resulting sample
is similar to the design used by the US national forest inventory (NFI). We demonstrated
model-based estimation using a sample of GLAS data in the US state of California,
where our estimate of biomass (211 Mg/hectare) was within the 1.4% standard error
of the design-based estimate supplied by the US NFI. The standard error of the GLAS-based
estimate was significantly higher than the NFI estimate, although the cost of the
GLAS estimate (excluding costs for the satellite itself) was almost nothing, compared
to at least US$ 10.5 million for the NFI estimate.

Conclusions

Global application of model-based estimation using GLAS, while demanding significant
consolidation of training data, would improve inter-comparability of international
biomass estimates by imposing consistent methods and a globally coherent sample frame.
The methods presented here constitute a globally extensible approach for generating
a simple random sample from the global GLAS dataset, enabling its use in forest inventory
activities.